title: Connectedness of PPI network neighborhoods identifies regulatory hub proteins.
abstract: BACKGROUND
  With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function.
  
  
  RESULTS
  We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease.
  
  
  BACKGROUND
  Code is available at http://bcb.cs.tufts.edu/hubcomps.
  
  
  BACKGROUND
  fox.andrew.d@gmail.com; slonim@cs.tufts.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, Tufts University, Medford, MA 02155, USA. fox.andrew.d@gmail.com
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Fox
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Hescott
authors.forename: Anselm C
authors.initials: AC
authors.lastname: Blumer
authors.forename: Donna K
authors.initials: DK
authors.lastname: Slonim
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000494:pharmacology; D004194:Disease / Q000235:genetics; D025141:Drug Resistance, Fungal; D020869:Gene Expression Profiling; D005839:Gentamicins / Q000494:pharmacology; D006801:Humans; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D015003:Yeasts / Q000187:drug effects / Q000235:genetics / Q000378:metabolism
chemical_list: D000900:Anti-Bacterial Agents; D005839:Gentamicins; D029701:Saccharomyces cerevisiae Proteins